{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201906</td>\n",
       "      <td>13387</td>\n",
       "      <td>12500</td>\n",
       "      <td>95375</td>\n",
       "      <td>306980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201907</td>\n",
       "      <td>13600</td>\n",
       "      <td>12500</td>\n",
       "      <td>91895</td>\n",
       "      <td>293948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201908</td>\n",
       "      <td>13785</td>\n",
       "      <td>12500</td>\n",
       "      <td>91631</td>\n",
       "      <td>289118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201909</td>\n",
       "      <td>13817</td>\n",
       "      <td>12500</td>\n",
       "      <td>87938</td>\n",
       "      <td>277901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201910</td>\n",
       "      <td>13730</td>\n",
       "      <td>12500</td>\n",
       "      <td>84654</td>\n",
       "      <td>269005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201911</td>\n",
       "      <td>13857</td>\n",
       "      <td>12500</td>\n",
       "      <td>81763</td>\n",
       "      <td>262185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201912</td>\n",
       "      <td>13938</td>\n",
       "      <td>12500</td>\n",
       "      <td>81142</td>\n",
       "      <td>260002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202001</td>\n",
       "      <td>14008</td>\n",
       "      <td>12500</td>\n",
       "      <td>75301</td>\n",
       "      <td>242944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202002</td>\n",
       "      <td>14096</td>\n",
       "      <td>12500</td>\n",
       "      <td>69030</td>\n",
       "      <td>223830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202003</td>\n",
       "      <td>14220</td>\n",
       "      <td>12500</td>\n",
       "      <td>63574</td>\n",
       "      <td>204853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202004</td>\n",
       "      <td>14368</td>\n",
       "      <td>12500</td>\n",
       "      <td>61856</td>\n",
       "      <td>203674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0   201906        13387          12500     95375     306980\n",
       "1   201907        13600          12500     91895     293948\n",
       "2   201908        13785          12500     91631     289118\n",
       "3   201909        13817          12500     87938     277901\n",
       "4   201910        13730          12500     84654     269005\n",
       "5   201911        13857          12500     81763     262185\n",
       "6   201912        13938          12500     81142     260002\n",
       "7   202001        14008          12500     75301     242944\n",
       "8   202002        14096          12500     69030     223830\n",
       "9   202003        14220          12500     63574     204853\n",
       "10  202004        14368          12500     61856     203674"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"JD_Count\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照职能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61872, 122)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_career=data_original.groupby(by='career').agg(\n",
    "    salary=pd.NamedAgg(column='monthly_salary', aggfunc='mean')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_career['salary']=data_career['salary'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>career</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>光学算法工程师</th>\n",
       "      <td>30666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT重建</th>\n",
       "      <td>26500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统架构师</th>\n",
       "      <td>22855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器学习</th>\n",
       "      <td>22601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>规划算法工程师</th>\n",
       "      <td>22124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adas</th>\n",
       "      <td>20456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLAM</th>\n",
       "      <td>20329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图像算法</th>\n",
       "      <td>19604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>算法工程师</th>\n",
       "      <td>18343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信号处理</th>\n",
       "      <td>18219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPGA</th>\n",
       "      <td>17431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>17079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSP</th>\n",
       "      <td>17056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>16764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>视觉软件工程师</th>\n",
       "      <td>16606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cae</th>\n",
       "      <td>16556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unity3d</th>\n",
       "      <td>14406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据分析</th>\n",
       "      <td>14197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>遥感</th>\n",
       "      <td>14191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般程序员</th>\n",
       "      <td>13320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>etl</th>\n",
       "      <td>13116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gis</th>\n",
       "      <td>12816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爬虫工程师</th>\n",
       "      <td>12783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生物信息工程师</th>\n",
       "      <td>11687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         salary\n",
       "career         \n",
       "光学算法工程师   30666\n",
       "CT重建      26500\n",
       "系统架构师     22855\n",
       "机器学习      22601\n",
       "规划算法工程师   22124\n",
       "adas      20456\n",
       "SLAM      20329\n",
       "图像算法      19604\n",
       "算法工程师     18343\n",
       "信号处理      18219\n",
       "FPGA      17431\n",
       "机器人       17079\n",
       "DSP       17056\n",
       "大数据       16764\n",
       "视觉软件工程师   16606\n",
       "cae       16556\n",
       "Unity3d   14406\n",
       "数据分析      14197\n",
       "遥感        14191\n",
       "一般程序员     13320\n",
       "etl       13116\n",
       "gis       12816\n",
       "爬虫工程师     12783\n",
       "生物信息工程师   11687"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career.sort_values(by='salary', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
